SciELO - Scientific Electronic Library Online

 
vol.59 número3Kaolin Bleaching by Leaching Using Phosphoric Acid SolutionsCytotoxic Constituents from the Stem Bark of Alvaradoa amorphoides índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Não possue artigos similaresSimilares em SciELO

Compartilhar


Journal of the Mexican Chemical Society

versão impressa ISSN 1870-249X

Resumo

BAGHEBAN SHAHRI, Fatemeh; NIAZI, Ali  e  AKRAMI, Ahmad. Application of Wavelet and Genetic Algorithms for QSAR Study on 5-Lipoxygenase Inhibitors and Design New Compounds. J. Mex. Chem. Soc [online]. 2015, vol.59, n.3, pp.203-210. ISSN 1870-249X.

A quantitative structure-activity relationship (QSAR) modeling was carried out for the prediction of inhibitory activity of 1-phenyl[2H]-tetrahydro-triazine-3-one analogues as inhibitors of 5-lipoxygenase. Partial least squares (PLS) algorithm was employed to model the relationships between molecular descriptors and inhibitory activity of molecules using the genetic algorithm (GA) method as variable selection tool. Pre-processing methods such as wavelet transform (WT) were also used to enhance the predictive power of multivariate calibration methods. To evaluate the models applied in this study (PLS, GA-PLS and WT-GA-PLS), the inhibitory activities of several compounds, not included in the modeling procedure, were predicted. The results of models showed high prediction ability with root mean square error of prediction 0.194, 0.161 and 0.140 for PLS, GA-PLS and WT-GA-PLS, respectively. The WT-GA-PLS method was employed to predict the inhibitory activity of the new inhibitor derivatives.

Palavras-chave : 1-phenyl[2H]-tetrahydro-triazine-3-one analogues; genetic algorithms; wavelet transform; QSAR; PLS.

        · resumo em Espanhol     · texto em Inglês

 

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons